NAME

Math::Histo::KDE - 1-Dimensional Kernel Density Estimation (KDE) Engine

SYNOPSIS

use Math::Histo::KDE;

# Construct KDE from sample points
my $kde = Math::Histo::KDE->new(
    samples   => [1.2, 2.3, 2.5, 3.1, 4.8, 5.0],
    kernel    => 'gaussian',     # or epanechnikov, uniform, triangular, biweight, cosine
    bw_method => 'silverman',    # or scott, manual
);

# Evaluate estimated probability density (PDF)
my $pdf = $kde->eval(2.5);

# Evaluate cumulative distribution (CDF)
my $cdf = $kde->cdf(2.5);

# Invert CDF for quantile
my $median = $kde->quantile(0.50);

# Generate random synthetic samples
my @samples = $kde->sample(100, 42);

# Construct directly from a Math::Histo histogram
my $h_kde = Math::Histo::KDE->from_histogram($histo);

DESCRIPTION

Math::Histo::KDE provides fast, non-parametric continuous density estimation for 1-dimensional datasets using standard kernel functions and automated bandwidth selection rules.